Keep AI-generated documentation accurate by treating it as a draft grounded in the current repository, updating it alongside the code change it describes, and having a maintainer verify its meaning before merge. Automate repeatable generation and mechanical checks in CI, but do not mistake a green check for proof that a sentence reflects how the software actually behaves.
Make documentation part of the code change
When a change alters user-visible behavior, documentation belongs in the same review flow. Google’s documentation guidance puts it plainly: change documentation in the same change list as the code change. A single pull request (PR) or change list gives reviewers a shared view of what changed and how users are meant to work with it.
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Start by identifying which documents depend on the affected behavior. Common candidates explain public interfaces, commands, configuration defaults, installation, deployment, workflows, or architecture. Keeping ownership or links between relevant code areas and docs can help teams spot those dependencies, although there is no one mapping scheme that suits every repository.
The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →When behavior changes, ask the author and AI assistant to identify affected docs and examples. Update them in the same PR when needed. If no documentation change is warranted, record that decision so reviewers can distinguish a deliberate assessment from an overlooked task. Google also advises maintaining a small, current set of docs rather than accumulating a larger set that is neglected.
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Give the AI evidence from the current repository
An AI assistant cannot reliably infer your project’s latest behavior from general knowledge or old documentation. Provide the relevant source files, schemas, tests, approved docs, version information, and the change being reviewed. GitHub’s guidance on customizing Copilot with repository instructions recommends using project documentation and recent pull requests as context.
Make the task specific: ask the assistant to identify which claims its proposed edits rely on, and to flag missing evidence rather than guess. GitLab cautions against inventing details such as command syntax, API parameters, or interface names; its guidance for AI-assisted documentation is a useful reminder to check such details against the project.
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- Source code and schemas: establish the interfaces, defaults, and structures the project currently defines.
- Tests: help reveal expected behavior and exercise documented examples where coverage permits.
- Approved documentation and version details: preserve the project’s conventions and avoid mixing instructions for different releases.
- The current change: show what changed, so the assistant can focus on documentation affected by that change rather than rewriting unrelated pages.
Automate checks that have a reliable source of truth
Automation is most dependable when a fact can be checked against a structured source. If API reference details already live in schemas or source definitions, generate the reference from those inputs rather than maintaining the same facts manually in multiple places. Likewise, if a repository has designated source files for generated guidance, regenerate the output from those sources instead of editing the generated copy independently.
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Depending on the repository, CI can also run link checks, formatting checks, example builds or tests, and generated-file consistency checks. Choose checks that match the documentation and can produce a meaningful pass or failure. These checks can catch broken links, stale generated output, invalid examples, or formatting problems; they cannot determine by themselves whether prose accurately explains intent or real-world behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Have a person review the claims and examples
Before merging AI-assisted documentation, a maintainer should verify that it describes current behavior and the correct version. Read commands, paths, parameters, defaults, and interface names as if you were going to follow them. Check examples against the implementation or run them where practical, and review any security or permission boundaries the instructions describe.
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This is not a responsibility that transfers to the AI or to CI. CMS guidance on AI in application development states that humans remain responsible for outcomes and must apply critical oversight. Microsoft’s responsible AI guidance makes the parallel point for code: committed AI-generated code is your code, and developers should read, understand, and test changes.
Apply the same standard to explanations. Automation can refresh facts represented in code or schemas; it cannot reliably decide whether a rationale, operational judgment, or tradeoff is still valid. Keep a knowledgeable owner responsible for those explanations, and ask whether a change altered the decision they describe.
Use a review checklist before merge
- Has someone identified the documentation impact of each changed public behavior?
- Are the instructions grounded in the repository’s current code and the version they describe?
- Do commands, paths, parameters, defaults, and named interface elements exist as written?
- Can the examples be executed or checked, and do they represent current behavior?
- Were generated files regenerated from their designated source rather than edited inconsistently?
- Did the change update the docs in the same review, or record why no update was needed?
- Has a human maintainer read and approved the factual claims?
What automation can—and cannot—establish
Repository context, generation, and CI checks reduce opportunities for drift by making documentation changes easier to detect and repeatable facts easier to refresh. They do not establish an accuracy rate or guarantee that documentation stays correct; the guidance cited here is operational advice, not a comparative study measuring those outcomes. Accuracy still depends on connecting the docs to the current behavior and having a person review what the checks cannot judge.
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